{"title":"Designing Chemical Reaction Arrays Using Phactor and ChatGPT","authors":"Babak Mahjour, Jillian Hoffstadt and Tim Cernak*, ","doi":"10.1021/acs.oprd.3c00186","DOIUrl":null,"url":null,"abstract":"<p >High-throughput experimentation is a common practice in the optimization of chemical synthesis. Chemists design reaction arrays to optimize the yield of couplings between building blocks. Popular reactions used in pharmaceutical research include the amide coupling, Suzuki coupling, and Buchwald–Hartwig coupling. We show how the artificial intelligence (AI) language model ChatGPT can automatically formulate reaction arrays for these common reactions based on the literature corpus it was trained on. Critically, we showcase how ChatGPT results can be directly translated into inputs for the management software phactor, which enables automated execution and analysis of assays. This workflow is experimentally demonstrated, with modest to excellent yields of products obtained in each instance on the first attempt.</p>","PeriodicalId":55,"journal":{"name":"Organic Process Research & Development","volume":"27 8","pages":"1510–1516"},"PeriodicalIF":3.1000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Organic Process Research & Development","FirstCategoryId":"92","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.oprd.3c00186","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
引用次数: 6
Abstract
High-throughput experimentation is a common practice in the optimization of chemical synthesis. Chemists design reaction arrays to optimize the yield of couplings between building blocks. Popular reactions used in pharmaceutical research include the amide coupling, Suzuki coupling, and Buchwald–Hartwig coupling. We show how the artificial intelligence (AI) language model ChatGPT can automatically formulate reaction arrays for these common reactions based on the literature corpus it was trained on. Critically, we showcase how ChatGPT results can be directly translated into inputs for the management software phactor, which enables automated execution and analysis of assays. This workflow is experimentally demonstrated, with modest to excellent yields of products obtained in each instance on the first attempt.
期刊介绍：
The journal Organic Process Research & Development serves as a communication tool between industrial chemists and chemists working in universities and research institutes. As such, it reports original work from the broad field of industrial process chemistry but also presents academic results that are relevant, or potentially relevant, to industrial applications. Process chemistry is the science that enables the safe, environmentally benign and ultimately economical manufacturing of organic compounds that are required in larger amounts to help address the needs of society. Consequently, the Journal encompasses every aspect of organic chemistry, including all aspects of catalysis, synthetic methodology development and synthetic strategy exploration, but also includes aspects from analytical and solid-state chemistry and chemical engineering, such as work-up tools，process safety, or flow-chemistry. The goal of development and optimization of chemical reactions and processes is their transfer to a larger scale; original work describing such studies and the actual implementation on scale is highly relevant to the journal. However, studies on new developments from either industry, research institutes or academia that have not yet been demonstrated on scale, but where an industrial utility can be expected and where the study has addressed important prerequisites for a scale-up and has given confidence into the reliability and practicality of the chemistry, also serve the mission of OPR&D as a communication tool between the different contributors to the field.
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